human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "towards context aware interaction recognition"
  Abstract: Recognizing how objects interact with each other is a crucial task in visual recognition. If we define the context of the interaction to be the objects involved, then most current methods can be categorized as either: (i) training a single classifier on the combination of the interaction and its context; or (ii) aiming to recognize the interaction independently of its explicit context. Both methods suffer limitations: the former scales poorly with the number of combinations and fails to generalize to unseen combinations, while the latter often leads to poor interaction recognition performance due to the difficulty of designing a context-independent interaction classifier. To mitigate those drawbacks, this paper proposes an alternative, context-aware interaction recognition framework. The key to our method is to explicitly construct an interaction classifier which combines the context, and the interaction. The context is encoded via word2vec into a semantic space, and is used to derive a classification result for the interaction. #R##N#The proposed method still builds one classifier for one interaction (as per type (ii) above), but the classifier built is adaptive to context via weights which are context dependent. The benefit of using the semantic space is that it naturally leads to zero-shot generalizations in which semantically similar contexts (subjectobject pairs) can be recognized as suitable contexts for an interaction, even if they were not observed in the training set.

Connected through hyperedge HE-1 (induced by source paper "detecting human object interactions via functional generalization"):
  with members:
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "interact as you intend intention driven human object interaction detection"
    - "detecting unseen visual relations using analogies"
    - "women also snowboard overcoming bias in captioning models"
    - "ican instance centric attention network for human object interaction detection"
    - "zero shot object detection by hybrid region embedding"
    - "no frills human object interaction detection factorization layout encodings and training techniques"
    - "detecting visual relationships using box attention"

Connected through hyperedge HE-2 (induced by source paper "factorizable net an efficient subgraph based framework for scene graph generation"):
  with members:
    - "vip cnn visual phrase guided convolutional neural network"
    - "co attending free form regions and detections with multi modal multiplicative feature embedding for visual question answering"
    - "visual question generation as dual task of visual question answering"
    - "weakly supervised learning of visual relations"
    - "detecting visual relationships with deep relational networks"
    - "scene graph generation by iterative message passing"
    - "visual relationship detection with internal and external linguistic knowledge distillation"
    - "scene graph generation from objects phrases and region captions"

Connected through hyperedge HE-3 (induced by source paper "linknet relational embedding for scene graph"):
  with members:
    - "iterative visual reasoning beyond convolutions"
    - "couplenet coupling global structure with local parts for object detection"
    - "visual interaction networks"
    - "attention is all you need"
    - "neural motifs scene graph parsing with global context"
    - "a simple neural network module for relational reasoning"
    - "detecting visual relationships with deep relational networks"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"

Connected through hyperedge HE-4 (induced by source paper "image level attentional context modeling using nested graph neural networks"):
  with members:
    - "iterative visual reasoning beyond convolutions"
    - "graph r cnn for scene graph generation"
    - "relational inductive biases deep learning and graph networks"
    - "vip cnn visual phrase guided convolutional neural network"
    - "neural motifs scene graph parsing with global context"
    - "recurrent relational networks"
    - "a simple neural network module for relational reasoning"
    - "detecting visual relationships with deep relational networks"

Connected through hyperedge HE-5 (induced by source paper "scene graph generation from objects phrases and region captions"):
  with members:
    - "vip cnn visual phrase guided convolutional neural network"
    - "person search with natural language description"
    - "detecting visual relationships with deep relational networks"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"
    - "scene graph generation by iterative message passing"
    - "care about you towards large scale human centric visual relationship detection"
    - "ppr fcn weakly supervised visual relation detection via parallel pairwise r fcn"
    - "towards diverse and natural image descriptions via a conditional gan"

Connected through hyperedge HE-6 (induced by source paper "pixels to graphs by associative embedding"):
  with members:
    - "vip cnn visual phrase guided convolutional neural network"
    - "detecting visual relationships with deep relational networks"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"
    - "scene graph generation by iterative message passing"
    - "learning to detect human object interactions"
    - "on support relations and semantic scene graphs"
    - "discovering objects and their relations from entangled scene representations"
    - "mask r cnn"

Connected through hyperedge HE-7 (induced by source paper "care about you towards large scale human centric visual relationship detection"):
  with members:
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"
    - "learning to detect human object interactions"
    - "zero shot learning the good the bad and the ugly"
    - "visual translation embedding network for visual relation detection"
    - "attend in groups a weakly supervised deep learning framework for learning from web data"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "visual relationship detection with language priors"
    - "visual semantic role labeling"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.CV(Computer Vision and Pattern Recognition)